st: Re: st: RE: Truncated sample or Heckman selection‏

I agree on #1.
On #2, how is Ebru going to fit any kind of model with no data on predictors?
Nick
On Thu, Oct 4, 2012 at 7:50 PM, Millimet, Daniel <millimet@mail.smu.edu> wrote:
> 1. A fractional logit model is more appropriate when modeling percentages.
> 2. The data set up is in between the usual Heckman vs. truncated model setup. With the typical Heckman approach, X's are observed for all observations and there is no information on the missing outcome. With a truncated setup, we observe no X's, but have information at least on the range of values for the outcome. Here, you do know something about the value of Y for the censored observations as in the truncated setup, but you only observe a subset of the X's. To me, it sounds you could perhaps "invent" a new model that is a zero-inflated fractional logit model, since you have 1 set of regressors that impact perhaps the probability of no innovation, and then a second set of regressors that impacts the amount of innovation conditional on this being positive.
>
> Anyway, perhaps not the best answer.
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Ebru Ozturk
>
> I have a question that I cannot decide whether I should use truncated regression or Heckman sample selection.
> For instance, in the dataset, firms that produce any type of innovation (process or product) give information about other 'x' variables. In other words, firms that do not produce any innovation do not answer other questions as these questions are directly related to firms' innovation activities. So, the 'x' variables that I am interested in have no values only for those firms that do not produce innovation. But, I know the dependent (y) variable in both case, either firms produce innovation or not produce.
>
>
> I am planning to run tobit regression as the dependent variable is percentage between 0 - 100 and Heckman sample selection model to check selection bias. But, I can not decide whether it is truncated sample or Heckman sample selection.
>
> So, what do you think?
>
> Thank you very much, Ebru.
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